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This study aims to compare the quality of fat extracted from different priced dry pet food for adult cats through classical and instrumental methods: pressure differential scanning calorimetry (PDSC), Fourier transform infrared spectroscopy (FT-IR) or gas chromatography (GC). Fat extracted from pet food was examined for induction time (IT), fatty acid composition, free fatty acid (FFA) content and peroxide value with the use of PDSC, GC, acid–base and iodometric titration, respectively. FT-IR data from the selected spectral regions correlate with the value of oxidation IT or the content of FFA. This resulted in construction of a reference model for IT with the following statistical features: Rcalibration = 0.917 (RMSEC = 28.0) and Rvalidation = 0.841 (RMSEP = 34.6). For fatty acid content, model statistics were as follows: Rcalibration = 0.912 (RMSEC = 0.61) and Rvalidation = 0.856 (RMSEP = 0.75). Discriminant model that uses spectral data alone, calculated with performance index 83.7 allowed distinguishing the studied pet food samples due to the price. Studies conducted proved PDSC and IR as reliable analytical techniques to control and monitor the quality of dry pet food for cats. Considering quality of the studied samples, it was proved that low-priced pet foods can be stored longer than premium-priced ones, while former is nutritionally more beneficial for adult cats.

Drying behaviour of sawdust mixture was investigated in a convective dryer at 0.01 m/s and 25, 60, and 150°C air temperature. Sawdust mixture (60% of spruce and 40% of the second ingredient: beech, willow, ash, alder) and sawdust of spruce, beech, willow, alder and ash was used in the drying experiments. The sawdust mixture drying was affected by the drying of its ingredients. The experimental drying data were fitted to the theoretical, semi–theoretical, and empirical thin-layer models. The accuracies of the models were measured using the correlation coefficient, root mean square error, and reduced chi–square. All semi-theoretical and empirical models described the drying characteristics of sawdust mixture satisfactorily. The theoretical model of a sphere predicts the drying of sawdust mixture better than the theoretical model of an infinite plane. The effect of the composition of the sawdust mixture on the drying models parameters were also taken into account.

Growing demand for energy and care about natural environment is the motivation to find alternative sources of energy. One of them is biomass from cherry and morello cherry stones, which is food processing industry waste. Stones have fairly good physical properties regarding material for combustion. Cherry and morello cherry crop in Poland is at the level of 200 thousand tons yearly, and processing them generates a considerable amount of waste that consists of stones, which may be used for energy purposes. In the work, the initial water content (u), curves of combustion in different temperatures, percentage ash content, heat of combustion and percentage share of stone in the whole fruit were determined.

The goal of the study was to analyse the efficiency of two existing photovoltaic micro-installations with the power of approx. 40 kWp. The main factor differing the two installations being analysed was the arrangement of modules in relation to the sides of the world, one is south-west and the other is east-west. The total yield of electrical energy in 2017 from the south-west installation was higher and amounted to 34980 kWh. For the east-west installation the amount of energy generated was equal to 31180 kWh. 4 methods of forecasting electrical energy yield were discussed. Simple computational method proved to be the best method for both installations.

Coffee is one of the most popular beverages worldwide. The goal of this work is an estimation of the qualities of waste coffee grounds as a material used in biofuel production. There were selected two most popular coffee beans mixtures: arabica and arabica with robusta, as well as chocolate-flavoured coffee and green coffee (unroasted coffee beans). All types of coffee had approximately the same ash value its average oscillating between 2,59-3,21%. Calorific value of the dried waste coffee grounds after coffee percolation places it among very good energy materials

The article proposes to use machine learning as one of the areas of artificial intelligence to forecast the volume of biogas production from household organic waste. The use of five regression algorithms (Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regression, and Gradient Boosting Regression) to create an effective model for forecasting the volume of biogas production from household organic waste is considered. Based on the comparison of these algorithms by MSE and MAE indicators, the quality of training and their accuracy during forecasting are evaluated. The proposed algorithm for creating a model for forecasting biogas production volumes from household organic waste involves the implementation of 10 main and 3 auxiliary steps. Their advantage is that they aid in the performance of component data analysis, which is carried out based on the method of reducing the dimensionality of the data set, increasing interpretability, and minimizing the risk of data loss. An analysis of 2433 data is was carried out, which characterizes the formation of biogas from food (FW) and yard waste (YW) according to four features. Data preparation is performed using the Jupyter Notebook environment in Python. We select five machine learning algorithms to substantiate an effective model for forecasting volumes of biogas production from household organic waste. On the basis of the conducted research, the main advantages and disadvantages of the used algorithms for building forecasting models of biogas production volumes from household organic waste are determined. It is found that two models, "Random Forest Regressor" and "Gradient Boosting Regressor", show the best accuracy indicators. The other three models (Linear Regression, Ridge Regression, Lasso Regression) are inferior in accuracy and were not considered further. To determine the accuracy of the "Random Forest Regressor" and "Gradient Boosting Regressor" models, we choose the MSE and MAE indicators. The Random Forest Regressor model is found to be a more accurate model compared to the Gradient Boosting Regressor. This is confirmed by the fact that the MSE of the "Random Forest Regressor" model on the training data set is 7.14 times smaller than that of the "Gradient Boosting Regressor" model. At the same time, MAE is 2.67 times smaller in the "Random Forest Regressor" model than in the "Gradient Boosting Regressor" model. The MSE and MAE of both models are worse on the test data set, which indicates overtraining tendencies. The Gradient Boosting Regressor model has worse MSE and MAE than the Random Forest Regressor model on both the training and test data sets. It is established that the model based on the "Random Forest Regressor" algorithm is the most effective for forecasting the volume of biogas production from household organic waste. It provides MAE = 0.088 on test data and the smallest absolute errors in predictions. Further systematic improvement of the "Random Forest Regressor" model for forecasting biogas production volumes from household organic waste based on new data will ensure its accuracy and maintain competitive advantages. [ABSTRACT FROM AUTHOR]

Oat (Avena sativa L.) grains are an important source of protein, minerals and oil. They contain at least twice as much fat as other common cereals, such as wheat, barley or rye, and the fat contained in oat grains significantly contributes to their properties, including improved calorific value and nutritional quality. Heat treatment, particularly roasting, has long been considered the essential method of processing grains, as roasting effectively extends shelf life, provides a pleasant roasted flavor and improves taste. The paper covers the improving effect of the roasting process of the whole oat grain on the quality of the oil contained in the grains. A differential scanning calorimeter coupled with a high-pressure cell has been used to determine the oxidative stability of oil. A calorimetric bomb was applied to determine the whole grain calorific value and GC (Gas Chromatography) determined the fatty acid composition of oil. [ABSTRACT FROM AUTHOR]

The development of infant milk fat similar in its structure and composition to that present in human milk receives a considerable amount of attention and has become a challenge for the world's food industry. The aim of this work was an attempt to obtain structured lipids resembling human milk fat (HMF) by modifying mixtures of lard (L) and rapeseed oil (RO) catalyzed by Lipozyme RM IM. The enzymatic reactions were carried out at 70 °C for 4, 8, and 24 h. The second objective was to study the properties of obtained substitutes of human milk fat (HMFS) using gas chromatography, thin layer chromatography, and differential scanning calorimetry (DSC) methods. Chromatographic methods were used to determine the composition of fatty acids (FAs) and the regiospecific structure of triacylglycerols (TAG) of obtained HMFS. DSC methods were used to establish the oxidative stability of HMFS and to calculate their kinetic parameters of oxidation. Structured lipids obtained from a mixture of L and RO after 4 h interesterification in 70 °C were characterized by the closest FA content and their distribution in TAG to HMF. Interesterification influenced the decrease of the induction time of obtained structured lipids. [ABSTRACT FROM AUTHOR]

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